Paute River Basin Research Articles

Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin

The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make appropriate decisions in extreme environments. This study seeks to predict discharges in the Paute river basin through global teleconnection indices. Multiple Linear Regression (MLR) was obtained using three different methodologies: multicollinearity analysis, Principal Component Analysis (PCA), and correlation with monthly delays. It was shown that the principal component analysis scenario obtained the best predictive fits, specifically by including 41 indices and 20 components. For the scenario using monthly delays, the best delay occurs within a single month for most seasons. Finally, with the multicollinearity analysis scenario, better results were obtained using 41 indices, although essentially the performance corresponds to the number and indices of each model. Teleconnection indices are not sufficient when used as the only input variable for download modeling and prediction, giving mostly unsatisfactory results. However, a clear trend links the behavior of flows and indices, and it is possible to improve the models based on more climatic variables or with other predictive methods.

Hydro-morphological simulation of the stilling basin of Mazar dam with 3D schemes

In the development of water resources projects with large hydraulic infrastructures, alterations to the natural flow regime will increase the erosive processes on the discharge structures due to variability of the turbulent velocities and the particle transport. We analyzed the case of the Mazar dam, a project designed to regulate the solid and liquid flows that come from the Paute river basin. Erosive processes of great magnitude are generated in the stilling basin of Mazar dam, due to the intensity of turbulent velocities produced in extreme events. We applied the Delft3D numerical model to simulate the hydrodynamic and sediment transport conditions to evaluate the stability conditions of the Mazar dam stilling basin. With the results obtained, vulnerable zones have been identified in the stilling basin. We recommended mitigation measures with the installation of a geomembrane to reduce the erosive processes in the vulnerable zones.

Selection of an adequate functional diversity index for stream assessment based on biological traits of macroinvertebrates

Functional diversity (FD) is useful for the evaluation of freshwater ecosystems. The FD of macroinvertebrate families for river water quality (WQ) assessment in the Paute River Basin (PRB), Ecuador, was investigated. Macroinvertebrate samples and data about 26 physical, chemical, microbiological and hydro-geomorphological variables were available. Literature-based biological traits were allocated as scores to the macroinvertebrates data through fuzzy coding. The Generalised Additive Mixed Model (GAMM) was used to assess the performance of six FD indices using the referred 26 WQ descriptive variables. The best performing GAMM led to selecting the index based on functional dendrograms including the species community pool (wFDc) as the most suitable to characterise FD in the PRB. The sub-basins of the PRB were grouped in three classes applying Average Linkage Clustering (ALC) and using wFDc. The Random Forest (RF) algorithm was used with a global efficiency of 89% to assess the ALC clusters consistency and pre-identify the significant WQ descriptive variables, explaining most of the FD variability. The Kruskal-Wallis test was then applied to refine the outcomes of the previous analysis. Twelve WQ descriptive variables were finally identified as the best discriminant predictors for FD, including the riparian vegetation, electric conductivity, dissolved oxygen, total hardness, faecal coliforms and pH. It is believed that the implemented approach successfully assessed the stream WQ status of the PRB upon selecting a suitable macroinvertebrate FD index; as such, it could be applied to other tropical basins for WQ assessment.

Occurrence Prediction of Riffle Beetles (Coleoptera: Elmidae) in a Tropical Andean Basin of Ecuador Using Species Distribution Models

Genera and species of Elmidae (riffle beetles) are sensitive to water pollution; however, in tropical freshwater ecosystems, their requirements regarding environmental factors need to be investigated. Species distribution models (SDMs) were established for five elmid genera in the Paute river basin (southern Ecuador) using the Random Forest (RF) algorithm considering environmental variables, i.e., meteorology, land use, hydrology, and topography. Each RF-based model was trained and optimised using cross-validation. Environmental variables that explained most of the Elmidae spatial variability were land use (i.e., riparian vegetation alteration and presence/absence of canopy), precipitation, and topography, mainly elevation and slope. The highest probability of occurrence for elmids genera was predicted in streams located within well-preserved zones. Moreover, specific ecological niches were spatially predicted for each genus. Macrelmis was predicted in the lower and forested areas, with high precipitation levels, towards the Amazon basin. Austrelmis was predicted to be in the upper parts of the basin, i.e., páramo ecosystems, with an excellent level of conservation of their riparian ecosystems. Austrolimnius and Heterelmis were also predicted in the upper parts of the basin but in more widespread elevation ranges, in the Heterelmis case, and even in some areas with a medium level of anthropisation. Neoelmis was predicted to be in the mid-region of the study basin in high altitudinal streams with a high degree of meandering. The main findings of this research are likely to contribute significantly to local conservation and restoration efforts being implemented in the study basin and could be extrapolated to similar eco-hydrological systems.

Evaluation of the SWAT Model With Respect to Different Variables Within the Tomebamba River Sub-Basin Belonging to the Paute River Basin, Ecuador

The objective of this study is to evaluate the efficiency of the SWAT model with respect to simulations of nitrates, organic phosphorus, dissolved oxygen, sediments and evapotranspiration, taking as reference data observed in continuous monitoring stations. The results showed that the correlation between the simulated and observed data is practically null if a linear relationship were established between them (Pearson correlation coefficient). On the other hand, the simulations overestimated the values of evapotranspiration, dissolved oxygen and sediments; and underestimated the values of nitrates and organic phosphorus, according to the PBIAS%. The RSR statistical criterion indicates that the performance of the modeling is unsatisfactory for nitrates, evapotranspiration and dissolved oxygen; and it is very good for organic phosphorus and the sediments of the Llaviuco station, which was verified with the application of the NSE statistical criterion. It is very important to pay attention to the initial calibration of the model.

Implications of macroinvertebrate taxonomic resolution for freshwater assessments using functional traits: The Paute River Basin (Ecuador) case

AbstractAimThis study aimed at investigating the taxonomic resolutions (TRs) of benthic macroinvertebrates for freshwater assessments in the scope of the functional trait approach (FTA).LocationMacroinvertebrate samples were collected in 22 locations within the Paute River Basin (PRB), Ecuador, over three years (2010, 2011 and 2012).MethodsBiological traits were allocated as scores to the macroinvertebrate data (at genus level) through fuzzy coding, using published data. The scores of each genus were used to derive scores for the corresponding family. These two sets of scores were standardized and compared, they were similar in 82% of the cases. Functional diversity (FD) was described by the rRao index, which showed no significant differences between coarse (family level) and fine (genus level) TRs. Cluster analyses using the K‐means algorithm were performed to determine similarities between both rRao data sets. The WQ cluster number (K) was varied between 2 and 5 to determine a threshold K value (Kth), after which a WQ assessment differed as a function of the TR being used.ResultsKth was 3. Family‐level identification in the framework of the FTA in the PRB was suitable in detecting changes of macroinvertebrate assemblages (until Kth = 3).Main conclusionsThe proposed methodology could be implemented in other basins where decision‐makers could decide whether the level of functional trait data similarity is sufficient for WQ management purposes and whether the defined Kth is acceptable. The reliability of the key methodological steps was assessed using performance statistics that have rarely been applied to ecological studies. Despite related research performed in other regions, the present study is the first South American attempt to investigate the effects of TR of benthic macroinvertebrates on freshwater bioassessments using functional traits.

Estimating monthly air temperature using remote sensing on a region with highly variable topography and scarce monitoring in the southern Ecuadorian Andes

Monitoring of air temperature has implications in a wide range of environmental applications. Air temperature commonly measured with meteorological stations provides a high accuracy and temporal resolution for specific monitoring sites. However, in regions with highly variable topography and scare monitoring such as the case of the southern Ecuadorian Andes, these in situ data poorly describe the spatial variability of air temperature. Thus, remote sensing data has a great potential to estimate the spatial distribution of climatological variables due to the spatial continuity of the information. This research aims to estimate the spatial distribution of the monthly air temperature in the Paute river basin for the period 2014–2017, using statistical and geostatistical methods: linear regression (LR), random forest regression (RF), and regression kriging (RK), in addition to evaluate the use of altitude and other auxiliary variables (land surface temperature, latitude, and longitude). Cross-validation showed that RF performed better than LR as well as when using auxiliary variables compared to only the altitude (LR-altitude: RMSE=1.325 °C, P bias= −0.150%, r=0.775; LR-auxiliary variables: RMSE=1.265 °C, P bias=0.000% r=0.795; RF-altitude: RMSE=1.235 °C, P bias =0.200%, r=0.810; RF-auxiliary variables RMSE=1.205 °C, P bias =0.200%, r=0.820). The application of regression kriging was limited since less than 50% of the months had spatial autocorrelation in the regression model residuals. Nevertheless, in these months, regression kriging increased the estimation performance. The outcomes of this research work increase the understanding of the spatial distribution of monthly air temperature in the Paute river basin, which will improve hydrological modeling.

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador.

Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.

Determinants of Food Insecurity in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Eliminating food insecurity is one of humanity’s greatest global challenges. Thus, the purpose of this research was to analyze the factors that determine food insecurity in households in the rural area of the Paute River Basin, Azuay Province, Ecuador. Stratified sampling was used as the sampling method, with proportional affixation. Moreover, we employed the Latin American and Caribbean Household Food Security Measurement Scale (ELCSA). We estimated the main determinants of household food insecurity using two binomial logit models and one ordered logit model. For the analysis of the data, the respective statistical and econometric tests were employed. The results show that housing size and access to food security information are the most important determinants of food insecurity in the three predictive models applied in this research. This research contributes to the existing literature on food insecurity and provides important information for policymakers, especially regarding food insecurity in rural areas, which has profound economic and social implications.

Multivariate-statistics based selection of a benthic macroinvertebrate index for assessing water quality in the Paute River basin (Ecuador)

Abstract Multivariate statistics -Soft Independent Modelling of Class Analogies (SIMCA), Principal Components Analysis (PCA), Multiple Regression (MR)- were used to search for key biotic and water quality (WQ) variables within a dataset/matrix collected over a five-year period in the Paute River Basin (Ecuador). Benthic macroinvertebrates and 27 descriptive physical, chemical, microbiological, hydrological and geomorphological variables were collected from 64 monitoring sites across the basin. Nine macroinvertebrate biotic indices were calculated. The SIMCA method was applied to find the most accurate biotic index that best discriminated among less polluted (C1), moderately polluted (C2) and highly polluted (C3) sites. A cross-validation scheme was applied to evaluate the performance of the modelling process. Within the PCA that was further refined using a Kruskal-Wallis test, the key WQ variables that mostly contributed to the macroinvertebrate-based WQ classification were identified. The results showed that the Elmidae-Plecoptera-Trichoptera (ElmPT) index was the most accurate biotic classifier. Riparian vegetation and streambed heterogeneity were the best predictors of the C1 class, while the concentration of faecal coliforms, pH, temperature and dissolved oxygen, best predicted the C3 class. The reduction of the field monitored parameters could help designing more cost-effective but equally accurate future WQ monitoring schemes in the basin.

Climate Change Influences of Temporal and Spatial Drought Variation in the Andean High Mountain Basin

Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981–2005) and future period (2011–2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades.

Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Forestry management and water law: comparing Ecuador and Arizona

ABSTRACTThis article compares public–private partnerships dedicated to improving forestry management to protect water in the Paute River basin in Ecuador (FONAPA) and the Verde River basin in Arizona (the Four Forest Restoration Initiative). Both programmes create incentives for improved forestry management and suggest lessons for water management in general but may face legal challenges that require reforms. While there is scope for mutual learning between the programmes, such cross-fertilization is inhibited by differences in the legal status of water and forest resources in the two systems.

Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm

A non-supervised (k-means) and a supervised (k-Nearest Neighbour in combination with genetic algorithm optimisation, k-NN/GA) pattern recognition algorithms were applied for evaluating and interpreting a large complex matrix of water quality (WQ) data collected during five years (2008, 2010–2013) in the Paute river basin (southern Ecuador). 21 physical, chemical and microbiological parameters collected at 80 different WQ sampling stations were examined. At first, the k-means algorithm was carried out to identify classes of sampling stations regarding their associated WQ status by considering three internal validation indexes, i.e., Silhouette coefficient, Davies-Bouldin and Caliński-Harabasz. As a result, two WQ classes were identified, representing low (C1) and high (C2) pollution. The k-NN/GA algorithm was applied on the available data to construct a classification model with the two WQ classes, previously defined by the k-means algorithm, as the dependent variables and the 21 physical, chemical and microbiological parameters being the independent ones. This algorithm led to a significant reduction of the multidimensional space of independent variables to only nine, which are likely to explain most of the structure of the two identified WQ classes. These parameters are, namely, electric conductivity, faecal coliforms, dissolved oxygen, chlorides, total hardness, nitrate, total alkalinity, biochemical oxygen demand and turbidity. Further, the land use cover of the study basin revealed a very good agreement with the WQ spatial distribution suggested by the k-means algorithm, confirming the credibility of the main results of the used WQ data mining approach.

Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.

Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes

Abstract. Investigation was made on the climate change signal for hydrometeorological and hydrological variables along the Paute River basin, in the southern Ecuador Andes. An adjusted quantile perturbation approach was used for climate downscaling, and the impact of climate change on runoff was studied for two nested catchments within the basin. The analysis was done making use of long daily series of seven representative rainfall and temperature sites along the study area and considering climate change signals of global and regional climate models for IPCC SRES scenarios A1B, A2 and B1. The determination of runoff was carried out using a lumped conceptual rainfall–runoff model. The study found that the range of changes in temperature is homogeneous for almost the entire region with an average annual increase of approximately +2.0 &degC. However, the warmest periods of the year show lower changes than the colder periods. For rainfall, downscaled results project increases in the mean annual rainfall depth and the extreme daily rainfall intensities along the basin for all sites and all scenarios. Higher changes in extreme rainfall intensities are for the wetter region. These lead to changes in catchment runoff flows, with increasing high peak flows and decreasing low peak flows. The changes in high peak flows are related to the changes in rainfall extremes, whereas the decreases in the low peak flows are due to the increase in temperature and potential evapotranspiration together with the reduction in the number of wet days.

Leukemia in children and youths of the Azuay province, Ecuador: 2000–2010

We mapped leukemia risk among children and youths in the Azuay province, Rio Paute river basin, Ecuador, in 2000–2010, using a Bayesian disease mapping model. We assessed the comprehensiveness of the list of leukemia cases from the Sociedad de Lucha contra el Càncer en el Ecuador (SOLCA) Hospital in Cuenca, the only referral center for oncology in the whole Rio Paute area, by comparison to the Quito cancer registry. Risk of leukemia did not vary significantly by canton within the Azuay province. However, a moderate increase in risk of borderline statistical significance was observed in the city of Cuenca and particularly among males in a heavily industrialized parish, who had an almost eight-fold excess (95% CI 3.03, 20.39, p = 0.01) of AML. Analytical studies are warranted to properly address specific etiological factor of leukemia among children and youths of the Azuay province of Ecuador.

Decadal oscillations in rainfall and air temperature in the Paute River Basin—Southern Andes of Ecuador

In the Andes environment, rainfall and temperature can be extremely variable in space and time. The determination of climate variability and climate change needs a special assessment for water management. This paper examines the anomalies of observed monthly rainfall and temperature data from 25 to 16 stations, respectively, from the early 1960s to the 1990s. The stations are located in the Rio Paute Basin in the Ecuador’s Southern Andes. All stations are within the elevation band 1,800 and 4,200 m a.s.l. and affected by the Tropical Pacific, Amazon, and Tropical Atlantic climate. Anomalies in quantiles were determined for each station and their significance tested. In addition, their correlations with different external climatic influences were studied for anomalies in annual and 3-month seasonal block periods. The results show similar temperature variations for the entire region, which are highly influenced by the El Nino–Southern Oscillation, especially during the December–February season. During June–August, the correlation is weaker showing the influence of other climate factors. Higher temperature anomalies are found at the high elevation sites while at deep valley sites the anomalies are less significant. Rainfall variations depend, in addition to elevation, on additional factors such as the aspect orientation, slope, and hydrological regime. The highest and most significant rainfall anomalies are found in the eastern sites.

Evaluation of precipitation products over complex mountainous terrain: A water resources perspective

The availability of in situ measurements of precipitation in remote locations is limited. As a result, the use of satellite measurements of precipitation is attractive for water resources management. Combined precipitation products that rely partially or entirely on satellite measurements are becoming increasingly available. However, these products have several weaknesses, for example their failure to capture certain types of precipitation, limited accuracy and limited spatial and temporal resolution. This paper evaluates the usefulness of several commonly used precipitation products over data scarce, complex mountainous terrain from a water resources perspective. Spatially averaged precipitation time series were generated or obtained for 16 sub-basins of the Paute river basin in the Ecuadorian Andes and 13 sub-basins of the Baker river basin in Chilean Patagonia. Precipitation time series were generated using the European Centre for Medium Weather Range Forecasting (ECMWF) 40 year reanalysis (ERA-40) and the subsequent ERA-interim products, and the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset 1 (NCEP R1) hindcast products, as well as precipitation estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The Tropical Rainfall Measurement Mission (TRMM) 3B42 is also used for the Ecuadorian Andes. These datasets were compared to both spatially averaged gauged precipitation and river discharge. In general, the time series of the remotely sensed and hindcast products show a low correlation with locally observed precipitation data. Large biases are also observed between the different products. Hydrological verification based on river flows reveals that water balance errors can be extremely high for all evaluated products, including interpolated local data, in basins smaller than 1000 km 2. The observations are consistent over the two study regions despite very different climatic settings and hydrological processes, which is encouraging for extrapolation to other mountainous regions.

Space–time rainfall variability in the Paute basin, Ecuadorian Andes

AbstractDespite the importance of mountain ranges as water providers, knowledge of their climate variability is still limited, mostly due to a combination of data scarcity and heterogeneous orography. The tropical Andes share many of the main features of mountain ranges in general, and are subject to several climatic influences that have an effect on rainfall variability. Although studies have addressed the large‐scale variation, the basin scale has received little attention. Thus, the purpose of this study was to obtain a better understanding of rainfall variability in the tropical Andes at the basin scal, utilizing the Paute River basin of southern Ecuador as a case study. Analysis of 23 rainfall stations revealed a high spatial variability in terms of: (i) large variations of mean annual precipitation in the range 660–3400 mm; (ii) the presence of a non‐monotonic relation between annual precipitation and elevation; and (iii) the existence of four, sometimes contrasting, rainfall regimes. Data from seven stations for the period 1964–1998 was used to study seasonality and trends in annual, seasonal and monthly precipitation. Seasonality is less pronounced at higher elevations, confirming that in the páramo region, the main water source for Andean basins, rainfall is well distributed year round. Additionally, during the period of record, no station has experienced extreme concentrations of annual rainfall during the wet season, which supports the concept of mountains as reliable water providers. Although no regional or basin‐wide trends are found for annual precipitation, positive (negative) trends during the wet (dry) season found at four stations raises the likelihood of both water shortages and the risk of precipitation‐triggered disasters. The study demonstrates how variable the precipitation patterns of the Andean mountain range are, and illustrates the need for improved monitoring. Copyright © 2007 John Wiley & Sons, Ltd.